Method and system for a real-time counting of a number of persons in a crowd by means of aggregated data of a telecommunication network

ABSTRACT

A method of estimating a number of persons gathering at an Area of Interest during a time interval on a day, wherein the Area of Interest is defined by an Area of Interest center and an Area of Interest radius and is covered by a mobile telecommunication network including plural communication stations each of which is configured to manage communications of user equipment in one or more served areas in a covered geographic region over which the mobile telecommunication network extends.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to crowd counting, i.e. to techniques forcounting or estimating the number of persons in a crowd. In the presentdescription and for the purposes of the present invention, by “crowd” itis meant a gathering of a certain number of people, gathered in acertain location for, e.g., attending at public events or happenings, ofthe most disparate nature, like for example (and non-exhaustively) livetelevision public happenings, artistic/entertaining performances,cultural exhibitions, theatrical plays, sports contests, concerts,movies, demonstrations and/or for visiting a place of particularinterest such as for example a museum, a monument, a building, and soforth.

Particularly, the present invention relates to crowd counting techniquesexploiting information provided by wireless or mobile telecommunicationnetworks.

Overview of the Related Art

In the tasks of urban planning, management of activities (e.g.,transport systems management and emergencies management), and tourismand local marketing, it is useful to have a knowledge of amounts ofpeople who gathered at certain locations or Areas of Interest (AoI forshort, e.g., a building, such as for example a stadium or a theatre or acinema, the surroundings thereof, a square or a street(s) of a city ortown or village, a district etc.), e.g. because they attended at publichappenings like shows (e.g., related to culture, entertaining, politicsor sports) that took place within the Area of Interest and/or forvisiting a place of interest (also denoted as point of interest) withinthe Area of Interest.

In case of one or more gatherings of people related to publichappenings, although the following considerations apply to gatherings ofpeople related to points of interest as well, this knowledge allows forexample a more effective planning of subsequent public happenings of thesame type. Particularly, this knowledge allows a more effective planningand managing of resources and activities (such as infrastructures,transport system and security) directly or indirectly related to similarpublic happenings that may take place in the future (such as for examplesports matches that regularly take place at a stadium). Moreover, from acommercial viewpoint, this knowledge allows a better management ofmarketing activities intended to promote similar events that may takeplace in the future.

Nowadays, mobile communication devices (referred to as mobile phones orUE in the following, including cellular phones, smartphones, tablets andthe like) have reached a thorough diffusion among the population of manycountries, and mobile phone owners almost always carry their mobilephones with them. Since mobile phones communicate with a plurality ofbase stations of the mobile phone networks, and each base station covers(i.e., serves) one or more predetermined serving areas, or cells, whichare known to the mobile communication services provider (e.g., mobilephone network owner or virtual mobile phone services provider), mobilephones result to be optimal candidates as tracking devices forcollecting data useful for identifying the amount of people who attendedto one or more public happenings.

In the art, many systems and methods have been proposed in order tocollect information about time and locations at, and in which, a UserEquipment (UE, e.g. a mobile phone, a smartphone, a tablet, etc.) of anindividual connects to the mobile phone network (e.g., for performing avoice call or sending a text message), and use such collectedinformation in order to derive information related to how many attendeesa certain public happening had.

For example, F. Calabrese, F. C. Pereira, G. Di Lorenzo, L. Liu, C.Ratti, “The Geography of Taste: Analyzing Cell-Phone Mobility in SocialEvents,” Pervasive Computing, LNCS 6030, Springer, 2010, pp. 22-37,discloses the analysis of crowd mobility during special events. Nearly 1million cell-phone traces have been analyzed and associated with theirdestinations with social events. It has been observed that the originsof people attending an event are strongly correlated to the type ofevent, with implications in city management, since the knowledge ofadditive flows can be a critical information on which to take decisionsabout events management and congestion mitigation.

Traag, V. A.; Browet, A.; Calabrese, F.; Morlot, F., “Social EventDetection in Massive Mobile Phone Data Using Probabilistic LocationInference”, 2011 IEEE Third International Conference on Privacy,Security, Risk and Trust (Passat), and 2011 IEEE Third InternationalConference on Social Computing (Socialcom), pp. 625, 628, 9-11 Oct.2011, focuses on unusually large gatherings of people, i.e. unusualsocial events. The methodology of detecting such social events inmassive mobile phone data is introduced, based on a Bayesian locationinference framework. More specifically, a framework for deciding who isattending an event is also developed. The method on a few examples isdemonstrated. Finally, some possible future approaches for eventdetection, and some possible analyses of the detected social events arediscussed.

Francesco Calabrese, Carlo Ratti, “Real Time Rome”, Networks andCommunications Studies 20(3-4), pages 247-258, 2006, discloses the RealTime Rome project, presented at the 10th International ArchitectureExhibition in Venice, Italy. The Real Time Rome project is the firstexample of a urban-wide real-time monitoring system that collects andprocesses data provided by telecommunications networks andtransportation systems in order to understand patterns of daily life inRome. Observing the real-time daily life in a town becomes a means tounderstanding the present and anticipating the future urban environment.

F. Manfredini, P. Pucci, P. Secchi, P. Tagliolato, S. Vantini, V.Vitelli, “Treelet decomposition of mobile phone data for deriving cityusage and mobility pattern in the Milan urban region”, MOX-Report No.25/2012, MOX, Department of Mathematics “F. Brioschi”, Politecnico diMilano, available at http://mox.polimi.it, discloses a geo-statisticalunsupervised learning technique aimed at identifying useful informationon hidden patterns of mobile phone use. These hidden patterns regarddifferent usages of the city in time and in space which are related toindividual mobility, outlining the potential of this technology for theurban planning community. The methodology allows obtaining a referencebasis that reports the specific effect of some activities on the Erlangdata recorded and a set of maps showing the contribution of eachactivity to the local Erlang signal. Results being significant forexplaining specific mobility and city usages patterns (commuting,nightly activities, distribution of residences, non systematic mobility)have been selected and their significance and their interpretation froma urban analysis and planning perspective at the Milan urban regionscale has been tested.

Ramon Caceres, James Rowland, Christopher Small, and Simon Urbanek,“Exploring the Use of Urban Greenspace through Cellular NetworkActivity”, 2nd Workshop on Pervasive Urban Applications (PURBA), June2012, discloses the use of anonymous records of cellular networkactivity to study the spatiotemporal patterns of human density in anurban area. This paper presents the vision and some early results ofthis effort. Firstly, a dataset of six months of activity in the NewYork metropolitan area is described. Secondly, a technique forestimating network coverage areas is presented. Thirdly, the usedapproach in analyzing changes in activity volumes within those areas isdescribed. Finally, preliminary results regarding changes in humandensity around Central Park are presented.

SUMMARY OF THE INVENTION

The Applicant has observed that, generally, method and systems known inthe art provide unsatisfactory results, as they are not able todetermine (or have a limited ability in determining) whether a UE ownerhas been in an Area of Interest (AoI) where one or more publichappenings have been held, for attending thereat or for other reasons(for example, because the UE owner resides or has a business inproximity of, or within, the area of interest). In addition, the resultsprovided by the known solutions are strongly influenced by the size ofthe area of interest selected for the analysis of the amount ofattendees at the one or more public happenings. In other words, if thearea of interest has a large size, a certain number of UE owners thatare not actually part of the crowd will be taken into account in theevaluation of the number of people in the crowd. Conversely, if the areaof interest has small size, a certain number of UE owners actually partof the crowd will be excluded from the evaluation of the number ofpersons in the crowd.

Therefore, subsequent planning and managing of resources and activities(of the type mentioned above) based on results obtained by the methodsand systems known in the art will achieve a limited efficiency due tothe limited accuracy thereof.

Moreover, known methods and systems based on the use of informationregarding positions occupied by each UE while connected to the mobilephone network (information that are collected by mobile phone networksserving the UE) could be intrusive of a privacy of the owners of the UE.

Indeed, such information allow knowing habits, routines of, and places(e.g., home and work places) daily frequented by, the UE owners.

Accordingly, the use of such information is thus usually highlyrestricted (to the extent of being prohibited) by laws issued by manyNational Authorities in order to protect the privacy of the UE owners.

In this respect, “anonymization” techniques known in the art and usedfor anonymizing information about the UE owners, in order to circumventprivacy issues, do not grant a satisfactory protection of the privacythereof.

Generally, the anonymization techniques comprise masking any identifiers(such as for example an International Mobile Equipment Identity—IMEI, anInternational Mobile Subscriber Identity—IMSI, or a Mobile SubscriberISDN Number) associated with the UE and/or the UE owner with encipheredidentifiers.

Nevertheless, an analysis of the information collected over a pluralityof days may be intrusive of UE owners since it anyways allowsidentifying sensitive information regarding habits, home and work placesof the UE owners and, possibly, the UE owners themselves by analyzingsuch sensitive information so obtained.

The Applicant has thus coped with the problem of devising a system andmethod adapted to overcome the problems affecting the prior artsolutions.

The Applicant has found that it is possible to determine the size of anoptimal area of interest on the basis of data related to the UE duringthe course of the one or more public happening and in a certain numberof days preceding the one or more public happening.

Moreover, the Applicant has found that it is possible to protect theprivacy of the UE owners by exploiting aggregated data regarding theusage of at least one mobile phone network.

For example, aggregated data exploitable by the present inventioncomprise a number of UE served by the mobile phone network within one ormore time interval (i.e., no information about single UE is providedthat may infringe upon UE owner privacy).

Preferably, by using aggregated data regarding separately one or moreserved areas of the mobile phone network it is possible to determine thesize of an optimal area of interest and then a number of people thatgathered within it with a high precision.

It should be noted that the knowledge of the number of UE served by themobile phone network within one or more time interval in the optimalarea generally does not correspond to the number of people in the crowd.Indeed, the number of UE served by the mobile phone network within oneor more time interval in the optimal area comprise UE owned by people inthe optimal area for reasons (e.g., work, people simply crossing theoptimal area) other than gathering in the crowd.

The Applicant has further found that it is possible to discern thenumber of people in the crowd within the optimal area from people thatare in the optimal area but are not in the crowd on the basis of theanalysis of aggregated data referred to the mobile phone network usageduring the gathering and during a number of days previous to the day inwhich the gathering of people occurred.

Particularly, one aspect of the present invention proposes a method ofestimating a number An of persons gathering at an Area of Interestduring a time interval on a day, wherein said Area of Interest isdefined by an Area of Interest center C and an Area of Interest radiusRs and is covered by a mobile telecommunication network having aplurality of communication stations each of which adapted to managecommunications of user equipment in one or more served areas in acovered geographic region over which the mobile telecommunicationnetwork provides services, the method comprising the steps of: a)subdividing the covered geographic region in a plurality of surfaceelements; b) defining a plurality of calculated radius values Rk of theArea of interest radius Rs, and, for each calculated radius value: c)identifying a number of relevant surface elements of the coveredgeographic region comprised within the Area of interest; d) computing afirst number Uk of User Equipment served by the mobile communicationnetwork during the time interval [Ts, Te] on the day g within the Areaof Interest based on aggregated data u_(q,t) regarding a usage of themobile communication network; e) computing a second number Upk of UserEquipment served by the mobile communication network during the timeinterval [Ts, Te] for each day gp of a predetermined number of previousP days preceding the day g within the Area of Interest based on theaggregated data u_(q,t) regarding the usage of the mobile communicationnetwork; f) combining the first number Uk of User Equipment and thesecond numbers Upk of User Equipment for obtaining a statisticalquantity Zk; g) computing a normalized statistical quantity Z′k bynormalizing the statistical quantity Zk with respect to the radii Ra ofthe relevant served areas; h) computing an optimum radius value Ro ofthe Area of Interest radius as the average of the calculated radiusvalues weighted by the normalized statistical quantity Z′k; i)estimating the number An of persons gathering within the Area ofInterest having the Area of Interest radius Rs equal to the optimumradius value Ro.

Preferred features of the present invention are set forth in thedependent claims.

In one embodiment of the invention, the aggregated data u_(q,t)regarding a usage of the mobile communication network comprise a numberof served User Equipment traffic load, number of voice calls, number ofSMS transmitted and/or volume of binary data exchanged within preferablyeach one of the communication stations of the mobile communicationnetwork.

In one embodiment of the invention, a surface element is identified as arelevant surface element if it verifies the following inequality:Dist(C,B)≤|Rs+Rk|,where C is the center of the Area of Interest, B is the center of thesurface element, Dist(C, B) is the geographical distance between thecenter of the Area of Interest C and the center of the surface elementB, Rs is the radius of the surface element, and Rk is the calculatedradius value.

In one embodiment of the invention, the method further comprises foreach calculated radius value: j) receiving a plurality of aggregateddata u_(q,t) regarding a usage of the mobile communication networkreferred to each one of said surface elements.

In one embodiment of the invention, the step i) of receiving a pluralityof aggregated data u_(q,t) regarding a usage of the mobile communicationnetwork for each one of said surface elements, comprises receiving a set{u_(q,t)} of aggregated data, each aggregated data u_(q,t) of the set{u_(q,t)} of the aggregated data being referred to a respectivereference time interval d_(t) which is a portion of an acquisitionperiod ΔT during which aggregated data u_(q,t) are collected.

In one embodiment of the invention, the step c) of computing a firstnumber Uk of User Equipment served by the mobile communication networkduring the observation time interval [Ts, Te] on the day g within theArea of Interest based on aggregated data u_(q,t) regarding a usage ofthe mobile communication network, comprises computing the first numberUk of User Equipment on the basis of sets {u_(q,t)} of aggregated datareferred to respective reference time intervals d_(t) comprised withinthe observation time interval [Ts, Te] on the day g, and wherein thestep d) of computing a second number Upk of User Equipment that has beenserved by the mobile communication network during the observation timeinterval [Ts, Te] for each day gpn of a predetermined number P ofprevious days preceding the day g within the Area of Interest based onthe aggregated data u_(q,t) regarding the usage of the mobilecommunication network, comprises computing each second number Upk ofUser Equipment on the basis of sets {u_(q,t)} of aggregated datareferred to respective reference time intervals d_(t) comprised withinthe observation time interval [Ts, Te] of the respective previous daygpn of the predetermined number P of previous days preceding the day g.

In one embodiment of the invention, the first number Uk of UserEquipment and/or each second number Upk of User Equipment may becomputed as total number, an average number, or a maximum number of UserEquipment in the relevant surface elements comprised in the Area ofInterest.

In one embodiment of the invention, the step e) of combining the firstnumber Uk of User Equipment and the second numbers Upk of User Equipmentfor obtaining a statistical quantity Zk comprises: combining the secondnumber Upk of User Equipment of each one of the previous days gpn inorder to determine an average User Equipment number μk and a UserEquipment number standard deviation σk.

In one embodiment of the invention, the step e) of combining the firstnumber Uk of User Equipment and the second numbers Upk of User Equipmentfor obtaining a statistical quantity further comprises: computing thestatistical quantity as:Zk=(Uk−μk)/σk,wherein Uk is the first number of User Equipment, μk is the average UserEquipment number and σk is the User Equipment number standard deviation.

In one embodiment of the invention, the plurality of calculated radiusvalues Rk ranges from a minimum radius value Rmin to a maximum radiusvalue Rmax, each calculated radius value being separated from a nextradius value by an iteration width Δ.

In one embodiment of the invention, the step h) of estimating a numberAn of persons gathering within the Area of Interest having the Area ofInterest radius Rs equal to the optimum radius value Ro comprises: k)defining a number of relevant surface elements among the surfaceelements comprised in the covered geographic region, wherein saidrelevant surface elements are surface elements at least partiallysuperimposed on the Area of Interest having the Area of Interest radiusequal to the optimum radius value.

In one embodiment of the invention, a surface element is identified as arelevant surface element if it verifies the following inequality:Dist(C,B)≤|Rs+Ro|,where C is the center of the Area of Interest, B is the center of thesurface element, Dist(C, B) is the geographical distance between thecenter of the Area of Interest C and the center of the surface elementB, Rs is the radius of the surface element, and Ro is the optimum radiusvalue.

In one embodiment of the invention, the step h) of estimating a numberAn of persons gathering within the Area of Interest having the Area ofInterest radius Rs equal to the optimum radius value Ro furthercomprises: computing a third number U|_(AOI) of User Equipment as anumber of User Equipment comprised within the relevant surface elementscomprised in the Area of Interest having the Area of Interest radius Rsequal to the optimum radius value Ro during the time interval on thebasis of the aggregated data u_(q,t) regarding a usage of the mobilecommunication network.

In one embodiment of the invention, computing a third number U|_(AOI) ofUser Equipment, comprises computing the third number U|_(AOI) of UserEquipment on the basis of sets {u_(q,t)} of aggregated data referred torespective reference time intervals d_(t) comprised within theobservation time interval [Ts, Te] on the day g.

In one embodiment of the invention, the step h) of estimating a numberAn of persons gathering within the Area of Interest having the Area ofInterest radius Rs equal to the optimum radius value Ro furthercomprises: computing a fourth number Up|_(AOI) of User Equipment as anumber of User Equipment comprised within the relevant surface elementscomprised in the Area of Interest having the Area of Interest radius Rsequal to the optimum radius value Ro for each day gpn of thepredetermined number P of previous days preceding the day on the basisof the aggregated data u_(q,t) regarding a usage of the mobilecommunication network.

In one embodiment of the invention, computing a fourth number Up|_(AOI)of User Equipment, comprises computing each fourth number Up|_(AOI) ofUser Equipment on the basis of sets {u_(q,t)} of aggregated datareferred to respective reference time intervals d_(t) comprised withinthe observation time interval [Ts, Te] of the respective previous daygpn of the predetermined number P of previous days preceding the day g.

In one embodiment of the invention, the third number U|_(AOI) of UserEquipment and/or each fourth number Up|_(AOI) of User Equipment may becomputed as total number, an average number, or a maximum number of UserEquipment in the relevant surface elements comprised in the Area ofInterest having the Area of Interest radius equal to the optimum radiusvalue.

In one embodiment of the invention, the step h) of estimating a numberAn of persons gathering within the Area of Interest having the Area ofInterest radius Rs equal to the optimum radius value Ro furthercomprises: combining the fourth number Up|_(AOI) of User Equipments ofeach one of the previous days gpn in order to determine a furtheraverage User Equipment number μ|_(AOI), the further average UserEquipment number μ|_(AOI) providing an indication of an average numberof persons normally comprised within the Area of Interest having theArea of Interest radius Rs equal to the optimum radius value Ro duringthe considered observation time interval [Ts, Te] in any days.

In one embodiment of the invention, the step h) of estimating a numberAn of persons gathering within the Area of Interest having the Area ofInterest radius Rs equal to the optimum radius value Ro furthercomprises: combining the third number U|_(AOI), of User Equipment andthe further average User Equipment number μ|_(AOI) in order to obtainthe number An of persons gathering in the Area of Interest having theArea of Interest radius Rs equal to the optimum radius value Ro.

In one embodiment of the invention, combining the third number U|_(AOI),of User Equipment and the further average User Equipment number μ|_(AOI)comprises subtract the further average User Equipment number μ|_(AOI)from the third number Up|_(AOI), of User Equipment.

Another aspect of the present invention proposes a system coupled with awireless telecommunication network for estimating a number of persons Angathering at an Area of Interest, the system comprising: a computationengine adapted to process data retrieved from a mobile telephonynetwork; a repository adapted to store data regarding interactionsbetween the User Equipment and the mobile telephony network, computationresults generated by the computation engine and, possibly, anyprocessing data generated by and/or provided to the system, and anadministrator interface operable for modifying parameters and/oralgorithms used by the computation engine and/or accessing data storedin the repository. Moreover, the system further comprises a memoryelement storing a software program product configured for implementingthe method of above through the system.

In one embodiment of the invention, the system further comprises atleast one user interface adapted to receive inputs from, and to provideoutput to a user of the system, the user comprising one or more humanbeings and/or one or more external computing systems subscriber of theservices provided by the system.

One of the advantages of the solution according to the present inventionis that it is computationally simple, involving just operations ofcounting and algebraic operations.

BRIEF DESCRIPTION OF THE DRAWINGS

These and others features and advantages of the solution according tothe present invention will be better understood by reading the followingdetailed description of an embodiment thereof, provided merely by way ofnon-limitative examples, to be read in conjunction with the attacheddrawings, wherein:

FIG. 1 is a schematic representation of a crowd estimation systemaccording to an embodiment of the present invention;

FIGS. 2A-2E are exemplary shapes that surface elements may takeaccording to an embodiment of the present invention;

FIGS. 3A-3B are examples of covered geographic regions associated with amobile communication network subdivided in corresponding sets of surfaceelements according to an embodiment of the present invention;

FIGS. 4A-4E are exemplary shapes that the AoI to be determined may takeaccording to an embodiment of the present invention;

FIGS. 5A-5D are relevant surface elements among surface elements inwhich the covered geographic region AoI is subdivided according to anembodiment of the invention, and

FIGS. 6A-6C are a schematic flowchart of a crowd estimation algorithmaccording to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

With reference to the drawings, FIG. 1 is a schematic representation ofa crowd estimation system, simply denoted as system 100 hereinafter,according to an exemplary embodiment of the present invention.

The crowd estimation system and method described in the following allowperforming an estimation of a number of persons in a crowd gathered forexample, in order to attend at one or more public happenings, of themost disparate nature, like for example (and non-exhaustively) livetelevision public happenings, artistic/entertaining performances,cultural exhibitions, theatrical plays, sports contests, concerts,movies, demonstrations and so forth.

In addition, it should be understood that the crowd estimation systemand method described in the following also allow performing anestimation of a number of persons in a crowd gathered for visiting aplace of particular interest such as for example a museum, a monument, ahistorical building and so forth.

The system 100 is coupled to a mobile communication network 105, such asa (2G, 3G, 4G or higher generation) mobile telephony network.

The mobile communication network 105 is able to provide communicationresources (e.g., a portion of an available communication bandwidth) toUser Equipment, UE in the following (e.g. a mobile phone, a smartphone,a tablet with 2G-3G-4G connectivity, etc.) requesting them in a coveredgeographic region (not detailed in FIG. 1, but described in thefollowing with reference to FIGS. 3A and 3B). In other words, UE withinthe covered geographic region may be served by the mobile communicationnetwork.

In one embodiment of the present invention, the covered geographicregion may comprise a whole territory covered (i.e., served) by themobile communication network 105, even though, in other embodiments ofthe present invention, a covered geographic region comprising only aportion of the whole territory covered by the mobile communicationnetwork 105 could be considered.

The mobile communication network 105 comprises a plurality (i.e., two ormore) of communication stations 105 a (e.g., radio base stations of themobile telephony network) deployed within the covered geographic region.

Each communication station 105 a is adapted to manage communications ofUE (not shown, such as for example mobile phones) in one or more servedareas or cells 105 b (in the example at issue, three cells are served byeach communication station 105 a).

Accordingly, the covered geographic region comprises the area of aplurality of the cells 105 b of the mobile communication network 105,for example in one embodiment of the invention the sum of the areas ofall the cells 105 b of the mobile communication network 105 builds upthe covered geographic region.

Differently, an (geographic) Area of Interest, AoI in brief 107schematized in FIG. 1 as the area within the dash-and-dot line 107extends over one or more cells 105 b of the mobile communication network105. The AoI 107 is an area within which the people gathered in a crowdfor example, in order to attend at one or more public happenings andwhose extent is determined by the crowd estimation algorithm of thepresent invention (as described in the following).

The system 100 is configured for receiving from the mobile communicationnetwork 105 aggregated data regarding the usage of the mobilecommunication network 105 within one or more reference time intervals(as described in the following).

The term ‘aggregated data’, as used in the present disclosure, indicatesdata regarding the operation of the mobile communication network 105,such as for example a number of served UE, traffic load, number of voicecalls, number of SMS transmitted, volume of binary data exchanged, etc.The aggregated data are typically used by an operator managing themobile communication network 105 for analysing general trends or values(e.g., changes of a number of served UE over time) in the exploiting ofresources (e.g., bandwidth and/or computational capabilities) of themobile communication network 105 without identifying each single UE (andtherefore the owners of the UE) that interacted with the mobilecommunication network 105.

Particularly, the aggregated data do not comprise any identifier of theUE served by the mobile communication network 105, therefore noindication about the UE owners identities, habits or frequented places(such as for example home and work places) may be obtained from theaggregated data provided by the mobile communication network 105, thusthe privacy of the UE owners is ensured.

In one embodiment of the invention, the aggregated data acquired by thesystem 100 from the mobile communication network 105 comprise anindication regarding a number of UE, i.e. indicative also of a number ofindividuals (the UE owners), located in the covered geographic region.

The covered geographic region just described comprises the Area ofInterest, AoI in brief, schematized in FIG. 1 as the area within thedash-and-dot line 107.

The AoI 107 (further described in the following) may generally comprisea core place (e.g., a stadium, a theater, a city square and so on) wherea public happening, which attracts a respective crowd, is taking placeand, possibly, surroundings (e.g., nearby parking lots, nearby streets,nearby transport stations and so forth) of the core place.

It should be noted that since the AoI 107 is comprised in the coveredgeographic region of the mobile communication network 105, thus UEwithin the AoI 107 may be served by the mobile communication network105.

Preferably, the aggregated data received from the mobile communicationnetwork 105 by the system 100 comprise an indication of a number of UEserved by the mobile communication network 105.

More preferably, the aggregated data received from the mobilecommunication network 105 by the system 100 comprise indications of theUE number served by the communication network 105 in a plurality ofsub-portions, or surface elements (described in the following), of thecovered geographic region. For example, each surface element maycomprise one of the cells 105 b, a group of two or more cells 105 b,and/or portions of the cells 105 b of the mobile communication network105 (e.g., in one embodiment of the invention the surface elements maybe shaped as squares having a side of 150m, therefore each one of thecells 105 b comprises more than one surface element especially inextra-urban areas where cells 105 b usually have a greater extent withrespect to cells 105 b in urban areas).

Generally, each communication station 105 a of the mobile communicationnetwork 105 is adapted to interact with any UE located within one of thecells 105 b served by such communication station 105 a (e.g.,interactions at power on/off, at location area update, atincoming/outgoing calls, at sending/receiving SMS and/or MMS, atInternet access etc.). Such interactions between UE and mobilecommunication network 105 will be generally denoted as events e_(i)(i=1, . . . , I; where I is an integer) in the following.

Therefore, aggregated data comprising the indication of a number of UEmay be computed by simply counting a number of UE that had at least oneinteraction with the mobile communication network 105. In other words,the aggregated data comprise an indication of the number of UE thatproduced at least an event e_(i) with one of the communication stations105 a that provides services over respective cells 105 b of the mobilecommunication network 105.

Alternatively, the indication of a number of UE may be based on trafficload experienced by the mobile communication network 105. Indeed, eachevent e_(i) in order to be performed, requires a portion ofcommunication resources (e.g., portions of a communication bandwidth)managed by the mobile communication network 105, i.e. each event e_(i)produces a certain amount of traffic load. Accordingly, the aggregateddata preferably provide an estimation of a number of UE on the basis ofa traffic load divided by an average UE traffic load (i.e., an averagetraffic load generated by each UE associated with the mobilecommunication network 105).

In the present disclosure it is assumed that aggregated data areprovided by the mobile communication network 105 periodically, e.g. atthe lapse of predetermined reference time intervals (e.g., every certainnumber of minutes, hours, on a daily or weekly basis) according to acapability of the mobile communication network 105 of collecting,processing and providing such aggregated data.

For example, in one embodiment of the present invention, the aggregateddata are collected by the mobile communication network 105 with aperiodicity equal to, or lower than, fifteen (15) minutes (i.e., thereference time intervals have a duration of fifteen minutes each), whichis a periodicity sustainable by computational capabilities of presentmobile communication networks.

Nevertheless, the mobile communication network 105 may be configured toprovide aggregated data asynchronously or, alternatively, aggregateddata may be provided by a third processing module (not shown) associatedwith the mobile communication network 105 for receiving the dataregarding operation of the mobile communication network 105 and with thesystem 100 for providing the aggregated data without departing from thescope of the present invention.

The system 100 comprises a computation engine 110 configured to processaggregated data retrieved from the mobile communication network 105, anda repository 115 (such as a database, a file system, etc.) configured tostore aggregated data received from the mobile communication network105, computation results generated by the computation engine 110 and,possibly, any processing data generated by and/or provided to the system100 (generally in a binary format). The system 100 is provided with anadministrator interface 120 (e.g., a computer) configured and operableto modify parameters and/or algorithms used by the computation engine110 and/or to access data stored in the repository 115.

Preferably, the system 100 comprises one or more user interfaces 125(e.g., a user terminal, a software running on a remote terminalconnected to the system 100) adapted to receive inputs from, and toprovide outputs to a user of the system 100. The term “user of thesystem” as used in the present disclosure may refer to one or more humanbeings and/or to external computing systems (such as a computer network,not shown) of a third party being subscriber of the services provided bythe system 100 and enabled to access the system 100—e.g., undersubscription of a contract with a service provider owner of the system100, and typically with reduced right of access to the system 100compared to the right of access held by an administrator of the system100 operating through the administrator interface 120.

It should be appreciated that the system 100 may be implemented in anyknown manner; for example, the system 100 may comprise a singlecomputer, or a network of distributed computers, either of physical type(e.g., with one or more main machines implementing the computationengine 110 and the repository 115 connected to other machinesimplementing administrator and user interfaces 120 and 125) or ofvirtual type (e.g., by implementing one or more virtual machines in acomputer network).

Preferably, the computation engine 110 processes aggregated data (asdiscussed in detail below) according to a crowd estimation algorithm(described in the following) implemented by a software program productstored in a memory element 110 a of the system 100, e.g. comprised inthe computation engine 110 in the example of FIG. 1, even though thesoftware program product could be stored in the repository 115 as well(or in any other memory element provided in the system 100).

In operation, the aggregated data may be continuously retrieved by thesystem 100 from the mobile communication network 105. For example,aggregated data may be transferred from the mobile communication network105 to the system 100 as they are generated, in a sort of “push”modality.

Alternatively, aggregated data may be collected by the mobilecommunication network 105 with a first periodicity (e.g., every 15minutes) and then packed and transferred to the system 100 with a secondperiodicity lower than the first periodicity (e.g., every hour), or onlyupon request by the system 100.

The aggregated data retrieved from the mobile communication network 105are preferably stored in the repository 115, where they are madeavailable to the computation engine 110 for processing.

The aggregated data are processed according to instructions provided bythe system administrator (through the administrator interface 120), forexample stored in the repository 115, and, possibly, according toinstructions provided by a user (through the user interface 125), asdescribed in the following. Finally, the computation engine 110 providesthe results of the processing performed on the aggregated data to theuser through the user interface 125, and optionally stores suchprocessing results in the repository 115.

It should be further noted that the method described in the presentdisclosure may be implemented by using any source of data (e.g.,provided by one or more among WiFi, WiMax, Bluetooth networks orcombinations thereof with mobile telephony networks) from which it ispossible to obtain aggregated data that could be related to a number ofperson within a predetermined area (e.g., the surface elements, or theAoI 107).

Turning now to FIGS. 2A-2E, they are exemplary shapes in which surfaceelements 205 of the covered geographic region associated with the mobilecommunication network 105 (i.e., the covered geographic region in whichthe mobile communication network 105 is able to serve the UE) may bemodeled according to an embodiment of the present invention.

For the purposes of the present invention, each surface element 205 ofthe geographic region covered by the mobile communication network 105may be modeled as a surface (as shown in FIG. 2A) having a respectivesurface center B and a respective surface radius Rs.

According to an embodiment of the present invention, generally thesurface center B and the surface radius Rs of the surface element 205are not related with a geographic position of the one or morecommunication stations 105 a or the positions of the cells 105 b of themobile communication network 105.

As previously noted, the surface elements 205 may extend over one ormore cells 105 b of the mobile communication network 105, or conversely,surface elements 205 may be smaller than a cell 105 b of the mobilecommunication network 105.

It should be noted that surface elements 205 are not limited to adisc-like shape, in facts, surface elements 205 may have the shape of a,preferably although not strictly necessarily regular, polygon. In thiscase, the surface center B corresponds to a center of mass (or centroid)of the polygon, while the surface radius Rs corresponds to a segmentadjoining the center of mass of the polygon, i.e. the surface center B,with a vertex of the polygon (as shown in FIGS. 2B and 2D) or with amidpoint of a side of the polygon (as shown in FIGS. 2C and 2E).

Alternatively, the mobile communication network 105 may be modeled bymeans of a Voronoi tessellation diagram, in which each Voronoi cellcorresponds to a surface elements 205 of the covered geographic regionassociated with the mobile communication network 105 (since Voronoitessellation diagrams are well known in the art, they are not discussedfurther herein).

Preferably, the modeling, the list and the number of surface elements205 of the covered geographic region associated with the mobilecommunication network 105 may be predetermined by the mobilecommunication network 105, by the system 100, or are inputted to thesystem 100 by the administrator through the administrator interface 120.

Considering FIGS. 3A and 3B, they are examples of covered geographicregions 300 and 300′ associated with the mobile communication network105 subdivided in corresponding sets of surface elements 205 ₁₋₉ and205′₁₋₉, respectively, according to an embodiment of the presentinvention.

The covered geographic region 300 shown in FIG. 3A has been subdividedin nine surface elements 205 ₁₋₉ having the shape of a regular polygon,i.e. a square. Conversely, the geographic region 300′ shown in FIG. 3Bhas been subdivided in nine surface elements 205′₁₋₉ having the shape ofirregular polygons.

Generally, the geometric features of the surface elements 205 ₁₋₉ and205 ₁₋₉ may be based upon a number of parameters of the geographicregion, such as for example urban features (presence and distribution ofstreets, wards, etc.) and/or natural features (presence anddistributions of rivers, hills, etc.). Moreover, other references and/ormapping systems (such as for example well-known network planningsoftware tools used by provider of the mobile communication network 105)may be considered for defining the shapes and sizes of the surfaceelements 205 ₁₋₉ and 205′₁₋₉ in addition or as an alternative to urbanand natural features.

It should be noted that nothing prevents to define shapes and sizes ofthe surface elements 205 ₁₋₉ and 205′₁₋₉ according to a distribution ofthe cells 105 b of the mobile communication network 105.

Furthermore, generally there are no relationships among number, shapesand sizes of the surfaces elements 205 ₁₋₉ and 205′₁₋₉ and the AoI 107.

In one embodiment of the present invention, square surface elements arepreferably used such as the surface elements 205 ₁₋₉ of the coveredgeographic region 300. Even more preferably, the surface elements 205₁₋₉ correspond to pixels determined during the network planning by thenetwork planning software tools mentioned above.

Indeed, square surface elements 205 ₁₋₉ allow simply subdividing thecovered geographic region 300, e.g. having determined a reference point(such as for example the surface center B) of a generic surface element205 _(q) (q=1, . . . , Q; where Q is a positive integer, Q=9 in theexample of FIG. 3A) and the size of the sides of the square surfaceelements 205 ₁₋₉, it is simply possible to determine the vertexes andthe surface centers B of all of the surface elements 205 ₁₋₉.

For example, the pixels used as surface elements may be shaped assquares having a side having a size comprised between 200 m and 50 msuch as 150 m. This ensures a good trade-off between the detail level ofthe covered geographic region 300 and the computational complexityrequired to analyze the aggregated data referred to the coveredgeographic region 300.

According to one embodiment of the present invention, the aggregateddata provided by the mobile communication network 105 comprise anindication regarding a number of UE (and therefore of UE owners) foreach one of the of the surfaces elements 205 ₁₋₉ and 205′₁₋₉.

In the following, reference is made only to the coverage geographic area300 and to the respective surface elements 205 ₁₋₉ of FIG. 3A for thesake of simplicity and brevity; however, it should be noted that similarconsiderations may be applied to the coverage geographic area 300′ andto the respective surface elements 205′₁₋₉ of FIG. 3B as well.

In one embodiment of the invention, for each generic surface element 205_(q) the system 100 receives from the mobile communication network 105 arespective aggregated UE number u_(q).

Preferably, the system 100 receives from the mobile communicationnetwork 105 a plurality of aggregated UE numbers u_(q,t) for eachgeneric surface element 205 _(q). Each UE number u_(q,t) of theaggregated UE numbers u_(q,t) is referred to a t-th reference timeinterval of a plurality of consecutive reference time intervals d_(t)(t=1, . . . , T; where T is a positive integer).

In other words, the system 100 receives a set {u_(q,t)} of Q aggregatedUE numbers u_(q,t) (one for each one of the surface elements 205 _(q);i.e., nine aggregated UE numbers u_(q,t) in the example of FIG. 3A),each set being referred to a reference time interval d_(t) ofconsecutive T reference time intervals d_(t), e.g. each set {u_(q,t)} ofQ aggregated UE numbers u_(q,t) is generated at time instants t_(t)corresponding to the end of a respective time interval d_(t).

For example, by considering an acquisition period ΔT twenty four hourslong (ΔT=24 hr), during which T sets {u_(q,t)} of Q aggregated UEnumbers u_(q,t) are received by the system 100 for storing and/orprocessing (as described in the following), and time interval d_(t)fifteen minutes long (d_(t)=15 min.), at the end of the acquisitionperiod ΔT, the system 100 has available T=96 sets {u_(q,t)} of Q=9aggregated UE numbers u_(q,t), one for each reference time intervald_(t). Indeed, the acquisition period ΔT is subdivided in 96 consecutivereference time intervals d_(t) that have the following structure:d₁=[00:00, 00:15), d₂=[00:15, 00:30), . . . , d₉₅=[23:30, 23:45), andd₉₆=[23:45, 00:00).

According to an embodiment of the present invention, the aggregated UEnumbers u_(q,t) are computed on the basis of traffic loads of each cell105 b of the mobile communication network 105 during the correspondingreference time interval d_(t).

Preferably, the aggregated UE number u_(q,t) is computed by combiningthe traffic load (e.g., in Erlang) measured at each one of the cells 105b comprised in the coverage geographic area 300 during the correspondingt-th reference time interval d_(t) with the average UE traffic loadestimated for UE in the cells 105 b (i.e., an average traffic loadgenerated by each UE associated with the mobile communication network105).

The traffic load of each cell 105 b is divided by the average UE trafficload, thus obtaining an estimation of the number of UE served by eachthe cell 105 b during the reference time interval d_(t). Subsequently,the number of UE served by cells 105 b are distributed among the surfaceelements 205 _(q) of the covered geographic region 300.

For example, the determination (i.e., the distribution) of the number ofUE within each one of the surface elements 205 _(q) of the coveredgeographic region 300 may be based on the method described in the paperby Francesco Calabrese, Carlo Ratti: “Real Time Rome”, Networks andCommunications Studies 20(3-4), pages 247-258, 2006 mentioned above onthe basis of the number of UE served by cells 105 b.

It is pointed out that the present invention is independent from thequantity used for determining the aggregated UE number u_(q,t). Indeed,aside the traffic load measured in Erlangs, other forms accounting thetraffic load may be used, e.g. a number of calls per cell 105 b, anumber of connections per cell 105 b or the number of unique UEconnected per cell 105 b (i.e., each referred to the corresponding t-threference time interval d_(t)).

It is also pointed out that the present invention is independent from afunction used to distribute the numbers of UE served by cells 105 bamong the surface elements 205 _(q) of the covered geographic region300.

In another embodiment of the present invention, the mobile communicationnetwork 105 has the knowledge of the number of UE connected to each oneof the cells 105 b, therefore, there is no need to determine the numberof UE through the traffic load, and the aggregated UE numbers u_(q,t)may be straightforwardly determined by adding together the number of UEserved by the cells 105 b comprised in the surface element 205 _(q)during the respective reference time interval d_(t).

Turning now to FIGS. 4A-4E, they are exemplary shapes that the AoI 107to be determined may take according to an embodiment of the presentinvention.

Generally, the AoI 107 of the public happening may be modeled as an areahaving an AoI center C and an AoI radius Ra. For example, the AoI 107may be delimited by a circumference centered in the AoI center C andhaving the AoI radius Ra as circumference radius (as shown in FIG. 4A).

It should be noted that the AoI 107 may have shapes different from thecircumference. For example, the AoI 107 may have the shape of a,preferably regular, polygon. In this case, the AoI center C correspondsto a center of mass (or centroid) of the polygon, while the AoI radiusRa corresponds to a segment adjoining the center of mass of the polygonwith a vertex of the polygon (as shown in FIGS. 4B and 4D) or with amidpoint of a side of the polygon (as shown in FIGS. 3C and 3E) in asimilar way as for the surface elements 205 modeling discussed above.

The AoI center C may be set (e.g., by a user through the user interface125 or by a system administrator through the administrator interface120) as a (geographical) central point of the AoI 107 (e.g., ageographical central point of the core place), as an address of the coreplace of the public happening, as a point provided by a mappingsoftware, such as web mapping services (e.g., Google Maps™,OpenStreetMap™, etc.).

As will be described in more detail in the following, the AoI radius Ramay take zero or negative values along with positive values. In case theAoI radius Ra takes zero or negative values, the AoI 107 is limited tothe AoI center C (i.e., the core place of the one or more publichappenings). The meaning of zero or negative values for the AoI radiusRa will be further clarified by reference to such zero or negativevalues in the embodiments described below.

The algorithm described in the following is configured to determine anoptimum radius value Ro for the AoI radius Ra of the AoI 107. In oneembodiment of the invention, the optimum radius value Ro is determinedby means of iterative steps starting from a minimum radius value Rmin toa maximum radius value Rmax (as described hereinbelow). Preferably, theminimum radius value Rmin and the maximum radius value Rmax are set bythe administrator of the system 100 through the administrator interface120.

In an embodiment of the present invention, on the basis of statisticalanalysis of empirical data regarding a plurality of past publichappenings performed by the Applicant, the minimum radius value Rmin isset equal to −1500 m (Rmin=−1500 m), while the maximum radius value Rmaxis set equal to 1500 m (Rmax=1500 m).

Having defined the shape of the surface elements 205 of the coveredgeographic region 300 covered by the mobile communication network 105and the shape of the AoI 107, the concept of relevant surface elements,i.e., a surface elements 205 q of the covered geographic region 300 thatis considered at least partially belonging to the AoI 107 according toan embodiment of the invention will be now be introduced.

FIGS. 5A-5D are relevant served areas or surface elements 505 a-d amongthe surface elements 205 q of the covered geographic region 300 withrespect to the AoI 107 according to an embodiment of the invention.

In one embodiment of the invention, given the AoI 107 having the AoIcenter C and the generic surface elements 205 q having the surfacecenter B and the surface radius Rs, the generic cell 105 b may beconsidered a relevant surface element 505 a-d for the AoI 107 if thefollowing inequality is verified:Dist(C,B)≤|Rs+Ra|,  (1)where Dist(C, B) is the geographical distance between the AoI center Cand the surface center B.

According to the value of the AoI radius Ra of the AoI 107, inequality(1) may take three different meanings.

Namely, if the AoI radius Ra of the AoI 107 is greater than zero (i.e.,Ra>0), inequality (1) reduces to:Dist(C,B)≤(Rs+Ra)  (2)and the generic surface element 205 is considered a relevant surfaceelement (such as the case of relevant surface elements 505 a in FIG. 4A)for the AoI 107 having an AoI radius Ra greater than zero if the area ofthe AoI 107 and the generic surface element 205 are at least partiallysuperimposed (even if the AoI center C fall outside the generic surfaceelement 205).

If the AoI radius Ra of the AoI 107 is equal to zero (i.e., Ra=0) theinequality (1) reduces to:Dist(C,B)≤Rs  (3)

and the generic surface elements 205 q is considered a relevant surfaceelement (such as the case of relevant surface elements 505 b and 505 cin FIGS. 4B and 4C) for the AoI 107 having an AoI radius Ra equal tozero if the AoI center C of the AoI 107 is comprised in the genericsurface element 205 q.

Finally, if the AoI radius Ra of the AoI 107 is smaller than zero (i.e.,Ra<0) the generic surface element 205 q is considered a relevant surfaceelement (such as the case of relevant surface element 505 d in FIG. 4D)for the AoI 107 having an AoI radius Ra smaller than zero if the AoIcenter C of the AoI 107 is comprised within the generic surface element205 q at a distance from the surface center B equal to or smaller thanRs−|Ra|.

A (generic) public happening S, apart from being held at a specificlocation (i.e., the AoI 107), has a start time Ts and an end time Te.Consequently, for the purposes of the present invention the publichappening S has a relevant duration equal to an observation timeinterval [Ts, Te] (i.e., a time interval that starts at the a start timeTs and ends at the end time Te, lasting for Te−Ts time units, e.g.seconds, minutes or hours).

Both the start time Ts and the end time Te may be defined so as tocorrespond to the official (officially announced) start and end timesscheduled for that public happening S.

Nevertheless, the Applicant has observed that by anticipating the starttime Ts with respect to the official start time of the public happeningS it is possible to take into account the fact that people (i.e., UEowners that attend at the public happening S) arrive at the AoI 107before the official start time of the public happening S, which may beuseful for collecting data about a trend in time of a flow of attendeesarriving at the public happening S. For example, on the basis ofempirical data of previous public happenings, the Applicant has foundthat the start time Ts may be usefully anticipated to 60 minutes beforethe official start time of the public happening S in order to take intoaccount the trend of attendees arriving at the public happening S.

Similarly, the Applicant has observed that the end time Te may bedelayed with respect to the official end time of the public happening Sin order to take into account the fact that people leave the AoI 107after the official end time of the public happening, which may be usefulfor collecting data about a trend in time of a flow of attendees leavingthe public happening S. For example, on the basis of empirical data ofprevious public happenings, the Applicant has found that the end time Tsmay be usefully delayed by 30 minutes after the official end time of thepublic happening S in order to take into accounts the trend of attendeesleaving the public happening S.

Anyway, the administrator through the administrator interface 120,and/or the user through the user interface 125, may set any custom starttime Ts and end time Te for the public happening S. For example, thestart time Ts and the end time Te may be set in order to define theobservation time interval [Ts, Te] shorter than the effective durationof the public happening S (shorter than the duration of the whole event)in order to analyze a number or a variation of attendees to the publichappening S only during a sub-portion of the whole time duration of thepublic happening S.

In addition, during the course of the public happening the administratormay change in real-time the end time Te in order to obtain an instantnumber of attendees to the public happening S. For example, theadministrator may set the end time Te to a current time instant in orderto determine the number of attendees to the public happening S up to thecurrent time instant. Moreover, the administrator may determine thenumber of attendees to the public happening S up at a plurality ofsubsequent end times Te in order to identify a trend in the number ofattendees to the public happening S over time.

Having described the system 100 and the time (i.e., the start time Tsand the end time Te) and spatial (i.e., the AoI center C and AoI radiusRa of the AoI 107) characteristics of the public happening S, a crowdestimation algorithm (or crowd counting algorithm) according to anembodiment of the present invention will be now described, by makingreference to FIGS. 6A-6C, which are a schematic block diagram thereof.

A first portion of the crowd estimation algorithm is configured todetermine the optimum radius value Ro for the AoI radius Ra of the AoI107 on the basis of the data regarding the public happening Sconsidered.

Initially (step 602) the AoI center C, the observation day g and thestart time Ts and end times Te of the public happening S are inputted tothe system 100, e.g. by a user through the user interface 125 or by theadministrator through the administrator interface 120.

Afterwards (step 604), an iteration variable k is initialized to zero(i.e., k=0) and a calculated radius value Rk is initially set to theminimum radius value Rmin (i.e., Rk=Rmin). The iteration variable kaccounts for the number of iterations of the first portion of thealgorithm.

Next (step 606), the relevant surface elements 505 a-d for the AoI 107having a AoI radius Ra equal to the calculated radius value Rk (Ra=Rk)are identified by means of the inequality (1) as described above.

Afterwards (step 608), a total radius Rtotk is computed by combining theradii Rs of the relevant surface elements 505 a-d identified at previousstep 606. For example, a total radius is defined simply as:Rtotk=Σ _(r) Rsr,  (4)where Rsr denotes the radius of the r-th relevant surface element 505a-d (1≤r≤R, R being a positive integer) identified at step 606.

All the sets {u_(q,t)} of Q aggregated UE numbers u_(q,t) referred tothe observation day g during an observation time interval [Ts, Te](i.e., referred to time intervals d_(t) comprised in the observationtime interval [Ts, Te]) and referred to the relevant surface elements505 a-d determined at step 606 are retrieved (step 610) from therepository 115.

Subsequently (step 612), a first UE number Uk is computed as the numberof UE in the relevant surface elements 505 a-d that have been retrievedat previous step 606 on the basis of the sets {u_(q,t)} of Q aggregatedUE numbers u_(q,t) (the first UE number Uk depends on the relevantsurface elements and, therefore, on the calculated radius value Rk).

The first UE number Uk may be computed as a total number, an averagenumber, or a maximum (peak) number of UE in the relevant surfaceelements 505 a-d, for example according to a setting selected by theadministrator of the system 100 through the administrator interface 120and/or by the user of the system 100 through the user interface 125.

For example, the first UE number Uk as the total UE number in therelevant surface elements 505 a-d may be computed in the followingmanner. Firstly, the sum of the values of aggregated UE numbers u_(q,t)(determined at step 610) within each reference time intervals d_(t)comprised in the observation time interval [Ts, Te] in all the relevantsurface elements 505 a-d (determined at step 606) is computed.Subsequently, a sum of the values just computed for each reference timeintervals d_(t) comprised in the observation time interval [Ts, Te] isperformed. In other words, the first UE number Uk as the total UE numberin the relevant surface elements 505 a-d may be computed as:

$\begin{matrix}{{Uk} = {\sum\limits_{t \in {\lbrack{{Ts},{Te}}\rbrack}}\;{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}\; u_{q,t}} ).}}} & ( {5a} )\end{matrix}$

Similarly, the first UE number Uk as the average UE number in therelevant surface elements 505 a-d may be computed in the followingmanner. Firstly, the sum of the values of aggregated UE numbers u_(q,t)(determined at step 610) within each reference time intervals d_(t)comprised in the observation time interval [Ts, Te] in all the relevantsurface elements 505 a-d (determined at step 606) is computed.Subsequently, a sum of the values just computed for each reference timeintervals d_(t) comprised in the observation time interval [Ts, Te] isperformed. Finally, the just obtained value is divided by the number T′of reference time intervals d_(t) comprised in the observation timeinterval [Ts, Te]. In other words, the first UE number Uk as the averageUE number in the relevant surface elements 505 a-d may be computed as:

$\begin{matrix}{{Uk} = {\frac{1}{T^{\prime}}{\sum\limits_{t \in {\lbrack{{Ts},{Te}}\rbrack}}\;{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}\; u_{q,t}} ).}}}} & ( {5b} )\end{matrix}$

Conversely, the first UE number Uk as the maximum (peak) UE number inthe relevant surface elements 505 a-d may be computed in the followingmanner. Firstly, the sum of the values of aggregated UE numbers u_(q,t)(determined at step 610) within each reference time intervals d_(t)comprised in the observation time interval [Ts, Te] in all the relevantsurface elements 505 a-d (determined at step 606) is computed.Subsequently, the maximum value among the values just computed isselected as the first UE number Uk. In other words, the first UE numberUk as the maximum (peak) UE in the relevant surface elements 505 a-d maybe computed as:

$\begin{matrix}{{Uk} = {{\underset{t \in {\lbrack{{Ts},{Te}}\rbrack}}{Max}( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}\; u_{q,t}} )}\;.}} & ( {5c} )\end{matrix}$

It should be noted that the first UE number Uk regardless whether iscomputed as total number, an average number, or a maximum (peak) numberof UE in the relevant surface elements 505 a-d is always dependent onthe calculated radius value Rk used to determine the relevant surfaceelements 505 a-d to which the aggregated UE numbers u_(q,t) arereferred.

Similarly, all the sets {u′_(q,t)} of Q aggregated UE numbers u′_(q,t)referred to previous days gp preceding the observation day g during theobservation time interval [Ts, Te] and having taken place within therelevant surface elements 505 a-d determined at step 606 are retrieved(step 614) from the repository 115.

In one embodiment of the invention, for the public happening S a set ofP previous days gp (where 1≤p≤P and P is an integer number) precedingthe observation day g are considered. The number P of previous days gpconsidered is preferably set by the administrator (through theadministrator interface 120). In an embodiment of the present invention,the administrator sets the number P of previous days gp according to thestorage capabilities of the repository 115 (i.e., in order to be able tostore all the data regarding the P previous days gp) and/or on the basisof computational capabilities of the computation engine 110 (i.e., inorder to be able to process all the data regarding the P previous daysgp). Preferably, the administrator sets the number P of previous days gpalso on the basis of a statistical analysis of past public happenings ofthe same kind (i.e., cultural, entertaining, politics or sport publichappenings).

The Applicant has found that setting the number P of previous days gpequal to 6 (i.e., P=6) provides good results for most kind of publichappenings (although this should not be construed as limitative for thepresent invention).

Then (step 616), a second UE number Upk is computed, for each one of theprevious days gp, as the number of UE in the relevant surface elements505 a-d on the basis of the sets {u′_(q,t)} of Q aggregated UE numbersu′_(q,t) referred to relevant surface elements 505 a-d that have beenretrieved at previous step 606 (the second UE number Upk depends on therelevant surface elements 505 a-d and, therefore, on the calculatedradius value Rk).

Similarly to the first UE number Uk, each one of the second UE numbersUpk may be computed as a total number, an average number, or a maximum(peak) number of UE in the relevant surface elements 505 a-d, forexample according to a setting selected by the administrator of thesystem 100 through the administrator interface 120 and/or by the user ofthe system 100 through the user interface 125.

For example, the second UE numbers Upk as the total numbers of UE in therelevant surface elements 505 a-d may be computed in the followingmanner. Firstly, for each one of the previous days gp, the sum of thevalues of aggregated UE numbers u′_(q,t) (determined at step 614) withineach reference time intervals d_(t) comprised in the observation timeinterval [Ts, Te] during the considered previous day gp in all therelevant surface elements 505 a-d (determined at step 606) is computed.Subsequently, a sum of the values just computed for each reference timeintervals d_(t) comprised in the observation time interval [Ts, Te]during the considered the previous day gp is performed. In other words,the second UE number Upk for the previous day gp as the total UE numberin the relevant surface elements 505 a-d may be computed as:

$\begin{matrix}{{Upk} = {\sum\limits_{t \in {\lbrack{{Ts},{Te}}\rbrack}}\;{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}\; u_{q,t}^{\prime}} ).}}} & ( {6a} )\end{matrix}$

The second UE numbers Upk as the average UE numbers in the relevantsurface elements 505 a-d may be computed in the following manner.Firstly, for each one of the previous days gp, the sum of the values ofaggregated UE numbers u′_(q,t) (determined at step 614) within eachreference time intervals d_(t) comprised in the observation timeinterval [Ts, Te] during the considered previous day gp in all therelevant surface elements 505 a-d (determined at step 606) is computed.Subsequently, a sum of the values just computed for each reference timeintervals d_(t) comprised in the observation time interval [Ts, Te]during the considered the previous day gp is performed. Finally, thejust obtained value is divided by the number T′ of reference timeintervals d_(t) comprised in the observation time interval [Ts, Te]during the considered the previous day gp. In other words, the second UEnumbers Upk for the previous day gp as the average UE numbers in therelevant surface elements 505 a-d may be computed as:

$\begin{matrix}{{Upk} = {\frac{1}{T^{\prime}}{\sum\limits_{t \in {\lbrack{{Ts},{Te}}\rbrack}}\;{( {\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}u_{q,t}^{\prime}} ).}}}} & ( {6b} )\end{matrix}$

The second UE numbers Upk as the maximum (peak) UE numbers in therelevant surface elements 505 a-d may be computed in the followingmanner. Firstly, for each one of the previous days gp, the sum of thevalues of aggregated UE numbers u′_(q,t) (determined at step 614) withineach reference time intervals d_(t) comprised in the observation timeinterval [Ts, Te] during the considered previous day gp in all therelevant surface elements 505 a-d (determined at step 606) is computed.Subsequently, the maximum value among the values just computed isselected as the second UE number Upk for the considered previous day gp.In other words, the second UE number Upk for the considered previous daygp as the maximum (peak) UE in the relevant surface elements 505 a-d maybe computed as:

$\begin{matrix}{{Upk} = {{\underset{t \in {\lbrack{{Ts},{Te}}\rbrack}}{Max}( \;{\sum\limits_{q \in {\lbrack{1,Q}\rbrack}}\; u_{q,t}^{\prime}} )}\;.}} & ( {6c} )\end{matrix}$

Also in this case, the second UE numbers Upk regardless whether iscomputed as total number, an average number, or a maximum (peak) numberof UE in the relevant surface elements 505 a-d are always dependent onthe calculated radius value Rk used to determine the relevant surfaceelements 505 a-d to which the aggregated UE numbers u_(q,t) arereferred.

The second UE numbers Upk just computed are combined (step 618) in orderto determine an average UE number μk (with

${{\mu\; k} = {\sum\limits_{p = 1}^{P}\;{Upk}}},$thus the average UE number μk is clearly different from the second UEnumbers Upk even if they are computed as the average number of UE in therelevant surface elements 505 a-d) and a UE number standard deviation σk(with

$ {{\sigma\; k} = \sqrt{\frac{\sum\limits_{p = 1}^{P}( {{Upk} - {µ\; k}} )^{2}}{P}}} )$of the UE numbers within the relevant surface elements 505 a-d duringthe P previous days gp considered.

The average UE number pk and the UE number standard deviation σk arecombined (step 620) with the first UE number Uk in order to obtain a(statistical) quantity defined z-score Zk (which depends on thecalculated radius value Rk):Zk=(Uk−μk)/σk.  (7)The z-score Zk just computed is normalized (step 622) with respect tothe total radius Rtotk computed at step 608:Z′k=Zk/Rtotk  (8)

Next, the variable k is increased by unity (step 624; i.e., k=k+1) andthe calculated radius value Rk is increased (step 626):Rk=Rmin+kΔ,  (9)where Δ is an iteration step or width that may be defined by theadministrator (e.g., Δ=100 m), thus each calculated radius value Rk isseparated from the next calculated radius value by an iteration width Δ.It should be noted that the iteration width Δ define a maximum iterationvalue kmax for the iteration variable k—and, therefore, a maximum numberof iteration for determining the optimum radius value Ro—as:kmax=(|Rmin|+Rmax)/Δ.  (10)

It should be noted that the iteration width Δ may be used by the systemadministrator to adjust a granularity (i.e., fineness) with which theoptimum radius value Ro is determined, i.e. the smaller the iterationwidth Δ set by the administrator the higher the number of iterationsdefined by the maximum iteration value kmax and, thus, the finer agranularity of the algorithm.

In an embodiment of the present invention, since the minimum radiusvalue Rmin is set to −1500 m, the maximum radius value Rmax is set to1500 m and the iteration width Δ is set to 100 m the maximum iterationvalue kmax for the iteration variable k results to be equal to 30 and,therefore, the maximum number of iterations for determining the optimumradius value Ro is limited to 30.

Afterwards, it is checked (step 628) whether the calculated radius valueRk is lower than, or equal to, the maximum radius value Rmax:Rk≤Rmax.  (11)

In the affirmative case (exit branch Y of decision block 628), i.e. thecalculated radius value Rk is lower than, or equal to, the maximumradius value Rmax (i.e., Rk≤Rmax) operation returns to step 606 forstarting a new iteration of the first portion of the algorithm based onthe calculated radius value Rk just increased (at step 626) by a furtherk-th iteration width Δ.

In the negative case (exit branch N of decision block 628), i.e. thecalculated radius value Rk is greater than the maximum radius value Rmax(i.e., Rk≥Rmax), the optimum radius value Ro is computed (step 630) asthe average of the computed radius values Rk (with 1≤k≤kmax) weighted bythe normalized z-score Z′k computed at, or:

$\begin{matrix}{{Ro} = {\frac{\sum\limits_{k}\;{{{Rk} \cdot Z^{\prime}}k}}{\sum\limits_{k}{Z^{\prime}k}}.}} & (12)\end{matrix}$

The steps 606 to 628 of the first portion of the algorithm are iterateduntil the calculated radius value Rk is greater than the maximum radiusvalue Rmax (i.e., Rk>Rmax), and the optimum radius value Ro is computed(at step 630).

With the computation of the optimum radius value Ro at step 630 thefirst portion of the algorithm ends and a second portion of thealgorithm starts (at step 632, described in the following). At the endof the first portion of the algorithm, the AoI 107 is properly definedby the AoI center C and by the AoI radius Ra set equal to the optimumradius value Ro (Ra=Ro).

The second portion of the algorithm according to an embodiment of thepresent invention is configured to determine a number of people that isattending at the public happening S.

After the optimum radius value Ro has been computed at step 630, a setof actually relevant surface elements 505 a-d is defined (step 632).This set includes all the surface elements 205 of the mobilecommunication network 105 for which inequality (1) is verified when theAoI radius Ra is set equal to the optimum radius value Ro, or:Dist(C,B)≤|Rs+Ro|.  (13)

Then (step 634), all the sets {u_(q,t)} of Q aggregated UE numbersu_(q,t) referred to the observation day g during the observation timeinterval [Ts, Te] and having taken place within the actually relevantsurface elements 505 a-d determined at step 632 are retrieved from therepository 115.

Subsequently (step 636), a third UE number U|_(AOI) is computed as anumber of UE comprised within the relevant surface elements 505 a-dcomprised in the AoI 107 having the AoI radius Ra equal to the optimumradius value Ro during the observation time interval [Ts, Te] on thebasis of the sets {u_(q,t)} that have been retrieved at step 632.

Similarly to the first UE number Uk, the third UE number U|_(AOI) may becomputed as a total number, an average number, or a maximum (peak)number of UE in the relevant surface elements 505 a-d, for exampleaccording to a setting selected by the administrator of the system 100through the administrator interface 120 and/or by the user of the system100 through the user interface 125. It should be noted that, in thiscase, the third UE number U|_(AOI) is computed either as a total number,an average number, or a maximum (peak) number of UE in the relevantsurface elements 505 a-d within the AoI 107 having the AoI radius Raequal to the optimum radius value Ro, thus the third UE number U|_(AOI),is dependent on the optimum radius value Ro rather than on thecalculated radius value Rk (on which the first UE number Uk isdependent).

Once the third UE number U|_(AOI) has been computed, a persons number Ais initialized to zero (i.e., A=0) (step 638). The persons number Aaccounts for the number of people in the crowd, e.g. attendees at thepublic happening S (as described in the following).

All the sets {u′_(q,t)} referred to each one of the previous days gpwithin the observation time interval [Ts, Te] and having taken place inthe actually relevant surface element 505 a-d determined at step 632 areretrieved (step 640) from the repository 115.

Then (step 642), a fourth UE number Up|_(AOI) is computed for each oneof the P previous days gp as a number of UE comprised within therelevant surface elements 505 a-d comprised in the AoI 107 having theAoI radius Ra equal to the optimum radius value Ro during theobservation time interval [Ts, Te] on the basis of the sets {u′_(q,t)}that have been retrieved at step 640.

Also in this case, similarly to the second UE numbers Upk, each one ofthe fourth UE numbers Up|_(AOI) may be computed as a total number, anaverage number, or a maximum (peak) number of UE in the relevant surfaceelements 505 a-d, for example according to a setting selected by theadministrator of the system 100 through the administrator interface 120and/or by the user of the system 100 through the user interface 125. Itshould be noted that, in this case, the fourth UE numbers Up|_(AOI) arecomputed either as a total number, an average number, or a maximum(peak) number of UE in the relevant surface elements 505 a-d within theAoI 107 having the AoI radius Ra equal to the optimum radius value Ro;thus the fourth UE numbers Up|_(AOI), are dependent on the optimumradius value Ro rather than on the calculated radius value Rk (on whichthe second UE numbers Upk are dependent).

The fourth UE numbers Up|_(AOI) just computed are combined (step 644) inorder to determine a further average UE number μ|_(AOI) of the UE numberwithin the relevant surface elements 505 a-d. For example the furtheraverage UE number μ|_(AOI) may be computed as:

$\begin{matrix}{ \mu |_{AOI} = {{\sum\limits_{p = 1}^{P}{Up}}❘_{AOI}.}} & (14)\end{matrix}$

The further average UE number μ|_(AOI) provides an indication of anaverage number of persons normally comprised within the AoI 107 havingthe AoI radius Ra equal to the optimum radius value Ro during theconsidered observation time interval [Ts, Te] (i.e., people that do notgathers in the crowd).

It is pointed out that, while the further average UE number μn|_(AOI)computed as described above may provided a sort of limited accuracy(since two or more activities from a same UE within the consideredobservation time interval [Ts, Te] may be considered as each belongingto different UE), the further average UE number μn|_(AOI) provides anestimation of the average number of persons normally comprised withinthe AoI 107 having a satisfying accuracy provided with a lowcomputational complexity and ensuring a full respect of the privacy ofUE owners.

The person number A is then calculated (step 646) by combining (e.g.,subtracting) the further average UE number μ|_(AOI) determined at step644 with the third UE number U|_(AOI) determined at step 636, orA=U| _(AOI)−μ|_(AoI).  (15)

The person number A referred to the public happening S held on theobservation day g is stored (step 648) in the repository 115.

Preferably, the crowd estimation algorithm is terminated with theprovision (step 650) of the results, i.e. the persons number A to theuser through the user terminal 125 for inspection and/or furtherprocessing.

Advantageously, the crowd estimation algorithm according to the presentinvention may be configured to (possibly automatically) update in realtime the persons number A during the whole duration of the gathering ofpeople in the crowds. For example, the persons number A may be updated(either periodically or asynchronously) each time new aggregated dataare provided to the system 100 by the mobile communication network 105.

In summary, the crowd estimation algorithm (or crowd counting algorithm)comprises a first portion and a second portion.

The first portion of the crowd estimation algorithm comprises a firstcycle that scans (steps 608-628) all the computed radius values Rkbetween the minimum radius value Rmin and the maximum radius value Rmaxpublic happening. For each computed radius value Rk respective relevantsurface elements 505 a-d and a normalized z-score Z′k are determined. Onthe basis of such data (i.e., respective relevant surface elements 505a-d and the normalized z-score Z′k) the optimum radius value Ro isidentified. At the end of the first portion of the crowd estimationalgorithm, the AoI 107 having the optimum radius value Ro is defined.

The second portion of the crowd estimation algorithm computes the numberof UE (i.e., the third number U|_(AOI)) in the relevant surface elements505 a-d (i.e., comprised in the AoI 107) during the observation timeinterval [Ts, Te] in the observation day g, computes the number of UE(i.e., the fourth number Up|_(AOI)) in the same relevant surfaceelements 505 a-d (i.e., comprised in the AoI 107) during the observationtime interval [Ts, Te] in each one of the P previous days gp, an averagenumber of the latter (i.e., the further average number μ|_(AOI)) and,eventually, the number of person gathering in a crowd (i.e., the personsnumber A) on the basis of the numbers of UE in the relevant surfaceelements 505 a-d during the observation time interval [Ts, Te] in theobservation day g and the average number of the UE.

The crowd estimation system 100 and the crowd estimation algorithmaccording to an embodiment of the present invention allows a real-timecomputing of the number of persons gathering in a crowd, e.g. forattending at a public happening S, in a reliable way and properlyidentifying (by determining the optimum radius value Ro) an effectiveextension of AoI 107 associated with the public happening S.

The invention claimed is:
 1. A method of estimating a number of personsgathering at an Area of Interest during a time interval on a day,wherein the Area of Interest is defined by an Area of Interest centerand an Area of Interest radius and is covered by a mobiletelecommunication network including a plurality of communicationstations each of which is configured to manage communications of userequipment in one or more served areas in a covered geographic regionover which the mobile telecommunication network provides services, themethod comprising: a) subdividing the covered geographic region in aplurality of surface elements corresponding to cells of the mobiletelecommunication network; b) defining a plurality of calculated radiusvalues of the Area of interest radius, and, for each calculated radiusvalue: c) identifying a number of relevant surface elements of thecovered geographic region within the Area of interest; d) computing afirst number of User Equipment served by the mobile communicationnetwork during the time interval on the day within the Area of Interestbased on aggregated data indicating a ratio between traffic load andaverage user equipment traffic load of the mobile communication network;e) computing a second number of User Equipment served by the mobilecommunication network during the time interval for each day of apredetermined number of previous days preceding the day within the Areaof Interest based on the aggregated data; f) combining the first numberof User Equipment and the second numbers of User Equipment to obtain astatistical quantity; g) normalizing the statistical quantity to obtaina normalized statistical quantity, the statistical quantity beingnormalized with respect to the radii of the relevant served areas; h)computing an optimum radius value of the Area of Interest radius as anaverage of the calculated radius values weighted by the normalizedstatistical quantity; i) estimating the number of persons gatheringwithin the Area of Interest having the Area of Interest radius equal tothe optimum radius value.
 2. The method according to claim 1, wherein asurface element is identified as a relevant surface element if itverifies the following inequality:Dist(C,B)≤|Rs +Rk|, wherein C is the center of the Area of Interest, βis the center of the surface element, Dist(C, B) is the geographicaldistance between the center of the Area of Interest C and the center ofthe surface element B, Rs is the radius of the surface element, and Rkis the calculated radius value.
 3. The method according to claim 1,further comprising for each calculated radius value: j) receiving aplurality of aggregated data indicating ratios between traffic loads andaverage user equipment traffic loads referred to each one of the surfaceelements.
 4. The method according to claim 3, wherein j) receiving theplurality of aggregated data for each one of the surface elements,comprises: receiving a set of aggregated data, each aggregated data ofthe set of the aggregated data being referred to a respective referencetime interval which is a portion of an acquisition period during whichaggregated data are collected.
 5. The method according to claim 4,wherein the c) computing a first number of User Equipment served by themobile communication network during the observation time interval on theday within the Area of Interest based on the aggregated data regarding,comprises: computing a first number of User Equipment on the basis ofsets of aggregated data referred to respective reference time intervalscomprised within the observation time interval on the day; and whereinthe d) computing a second number of User Equipment that has been servedby the mobile communication network during the observation time intervalfor each day of a predetermined number of previous days preceding theday within the Area of Interest based on the aggregated data, comprises:computing each second number of User Equipment on the basis of sets ofaggregated data referred to respective reference time intervals withinthe observation time interval of the respective previous day of thepredetermined number of previous days preceding the day.
 6. The methodaccording to claim 5, wherein the first number of User Equipment and/oreach second number of User Equipment may be computed as a total number,an average number, or a maximum number of User Equipment in the relevantsurface elements in the Area of Interest.
 7. The method according toclaim 1, wherein the e) combining the first number of User Equipment andthe second numbers of User Equipment for obtaining a statisticalquantity comprises: combining the second number of User Equipment ofeach one of the previous days to determine an average User Equipmentnumber and a User Equipment number standard deviation.
 8. The methodaccording to claim 7, wherein the e) combining the first number of UserEquipment and the second numbers of User Equipment for obtaining astatistical quantity further comprises: computing the statisticalquantity as:Zk=(Uk−μk)/σk, wherein Uk is the first number of User Equipment, μk isthe average User Equipment number, and σk is the User Equipment numberstandard deviation.
 9. The method according to claim 1, wherein theplurality of calculated radius values ranges from a minimum radius valueto a maximum radius value, each calculated radius value being separatedfrom a next radius value by an iteration width.
 10. The method accordingto claim 1, wherein the h) estimating a number of persons gatheringwithin the Area of Interest having the Area of Interest radius equal tothe optimum radius value comprises: k) defining a number of relevantsurface elements among the surface elements in the covered geographicregion, wherein the relevant surface elements are surface elements atleast partially superimposed on the Area of Interest having the Area ofInterest radius equal to the optimum radius value.
 11. The methodaccording to claim 10, wherein a surface element is identified as arelevant surface element if it verifies the following inequality:Dist(C, B)≤|Rs+Ro|, wherein C is the center of the Area of Interest, βis the center of the surface element, Dist(C, B) is the geographicaldistance between the center of the Area of Interest C and the center ofthe surface element B, Rs is the radius of the surface element, and Rois the optimum radius value.
 12. The method according to claim 10,wherein the i) estimating a number of persons gathering within the Areaof Interest having the Area of Interest radius equal to the optimumradius value further comprises: computing a third number of UserEquipment as a number of User Equipment within the relevant surfaceelements in the Area of Interest having the Area of interest radiusequal to the optimum radius value during the time interval on the basisof the aggregated data regarding a usage of the mobile communicationnetwork.
 13. The method according to claim 12, wherein computing a thirdnumber of User Equipment, comprises: computing the third number of UserEquipment on the basis of sets of aggregated data referred to respectivereference time intervals within the observation time interval on theday.
 14. The method according to claim 12, wherein the third number ofUser Equipment and/or each fourth number of User Equipment may becomputed as a total number, an average number, or a maximum number ofUser Equipment in the relevant surface elements in the Area of Interesthaving the Area of Interest radius equal to the optimum radius value.15. The method according to claim 10, wherein the i) estimating a numberof persons gathering within the Area of Interest having the Area ofInterest radius equal to the optimum radius value further comprises:computing a fourth number of User Equipment as a number of UserEquipment within the relevant surface elements in the Area of Interesthaving the Area of Interest radius equal to the optimum radius value foreach day of the predetermined number of previous days preceding the dayon the basis of the aggregated data.
 16. The method according to claim15, wherein computing a fourth number of User Equipment, comprises:computing each fourth number of User Equipment on the basis of sets ofaggregated-data referred to respective reference time intervals withinthe observation time interval of the respective previous day of thepredetermined number of previous days preceding the day.
 17. The methodaccording to claim 15, wherein the i) estimating a number of personsgathering within the Area of Interest having the Area of Interest radiusequal to the optimum radius value further comprises: combining thefourth number of User Equipments of each one of the previous days todetermine a further average User Equipment number, the further averageUser Equipment number providing an indication of an average number ofpersons normally comprised within the Area of Interest having the Areaof Interest radius equal to the optimum radius values during theconsidered observation time interval in any days.
 18. The methodaccording to claim 17, wherein the i) estimating a number of personsgathering within the Area of Interest having the Area of Interest radiusequal to the optimum radius value further comprises: combining the thirdnumber of User Equipment and the further average User Equipment numberin order to obtain the number of persons gathering in the Area ofInterest having the Area of Interest radius equal to the optimum radiusvalue.
 19. The method according to claim 18, wherein the combining thethird number of User Equipment and the further average User Equipmentnumber comprises subtracting the further average User Equipment numberfrom the third number of User Equipment.
 20. A system coupled with awireless telecommunication network for estimating a number of personsgathering at an Area of Interest, the system comprising: a computationengine configured to process data retrieved from a mobile telephonynetwork; a repository configured to store data regarding interactionsbetween the User Equipment and the mobile telephony network, computationresults generated by the computation engine and processing datagenerated by and/or provided to the system; an administrator interfaceconfigured to modify parameters and/or algorithms used by thecomputation engine and/or accessing data stored in the repository; amemory element storing a software program product configured toimplement the method of claim 1 through the system.
 21. The systemaccording to claim 20, further comprising at least one user interfaceconfigured to receive inputs from, and to provide output to a user ofthe system, the user comprising one or more human beings and/or one ormore external computing systems subscriber of the services provided bythe system.